Adaptive fuzzy logic system for microwave cooking optimization

This study presents the development of an Adaptive Fuzzy Logic Control System for optimizing microwave cooking. Conventional microwave ovens typically rely on fixed settings that do not account for food variability. To address these issues, fuzzy logic was incorporated to dynamically adjust cooking parameter, specifically cooking time based on three input variables doneness level, quantity of food and initial temperature. The model uses a Mamdani type fuzzy inference system to produce accurate results that require overlapping membership functions and 27 rules in the form of IF THEN rules to determine the appropriate cooking time. The system’s flexibility enables it to learn and adapt over time through human engagement, resulting in enhanced customisation and energy efficiency. The model was implemented and simulated using MATLAB, demonstrating consistent, high-quality cooking results with reduced energy usage. This invention highlights how intelligent control systems can improve the sustainability, efficiency, and precision of home appliances.